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. Additional qualifications Experience with one or more of the following areas is meriting: Bayesian statistics, mathematical modelling, probabilistic machine learning, deep learning, large language models
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. Optimal transport is a key mathematical concept that allows us to understand notions like inference and sampling as dynamic processes of probability distributions. Building on the theoretical insights, we
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the Ph.D. Our recent works on AI privacy and security: Practical Bayes-Optimal Membership Inference Attacks, NeurIPS 2025, https://arxiv.org/pdf/ 24089 Secure Aggregation is Not Private Against Membership
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insights that inform biodiversity management. The project includes: · Apply of deep learning models to annotate bird and bat species from sound recordings. · Develop a Bayesian statistical
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-scale neural network models. While the developed methods will be broadly applicable, particular emphasis will be put on the problem of inferring gas dynamics in urban environments. Gas dynamics shape air
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and tectonic significance of these inferred basement structures remain insufficiently constrained. The overall objective of this PhD project is to test and refine existing structural models by
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inference and deployment costs (e.g., model compression/simplification and hardware-aware optimization). We are also interested in how resource-efficiency interacts with broader sustainability aspects
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. Our research tracks include: • Web application crawling and security scanning (Black Widow , Black Ostrich , and Spider-Scents ) • Security and privacy of browser extensions (FakeX and CodeX
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endosomal escape events. The tasks include developing, training, and validating deep learning–based models for event detection and vesicle tracking, and integrating these models into automated analysis and
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an international, stimulating, and collaborative research environment where your scientific career development is promoted. The project aims to track strain wide differences within human gut bacteria species in